Ignasi Vilajosana

Bootstrapping smart cities through a self-sustainable model based on big data flows

We have a clear idea today about the necessity and usefulness of making cities smarter, the potential market size, and trials and tests. However, it seems that business around Smart Cities is having difficulties taking off and is thus running short of projected potentials. This article looks into why this is the case and proposes a procedure to make smart cities happen based on big data exploitation through the API stores concept. To this end, we first review involved stakeholders and the ecosystem at large. We then propose a viable approach to scale business within that ecosystem. We also describe the available ICT technologies and finally exemplify all findings by means of a sustainable smart city application. Over the course of the article, we draw two major observations, which are seen to facilitate sustainable smart city development. First, independent smart city departments (or the equivalent) need to emerge, much like todays well accepted IT departments, which clearly decouple the political element of the improved city servicing from the underlying technologies. Second, a coherent three-phase smart city rollout is vital, where in phase 1 utility and revenues are generated; in phase 2 only-utility service is also supported; and in phase 3, in addition, a fun/leisure dimension is permitted.

The Power of Models: Modeling Power Consumption for IoT Devices

Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system.

REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports.

Design of a Motion Detector to Monitor Rowing Performance Based on Wireless Sensor Networks

Wireless Sensor Networks (WSN) have been applied to gather data from a large number of scenarios. WSN provide the potential to collect data at spatial and temporal scales that are many times not feasible with past instrumentation. Although many applications have been already developed, little has been done using WSN as infrastructure for sports oriented commercial applications.This paper studies the use of WSN to develop motion detectors. Particularly REMOTE, a sensor network based application that provides a detailed picture of a boat movement, rower’s individual performance, or his/her performance compared to other crew members is proposed. The application aims to analyze data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. The paper presents the architecture, software design and hardware infrastructure used to develop the application.

ZERO: Probabilistic Routing for Deploy and Forget Wireless Sensor Networks

As Wireless Sensor Networks are being adopted by industry and agriculture for large-scale and unattended deployments, the need for reliable and energy-conservative protocols become critical. Physical and Link layer efforts for energy conservation are not mostly considered by routing protocols that put their efforts on maintaining reliability and throughput. Gradient-based routing protocols route data through most reliable links aiming to ensure 99% packet delivery. However, they suffer from the so-called ”hot spot” problem. Most reliable routes waste their energy fast, thus partitioning the network and reducing the area monitored. To cope with this ”hot spot” problem we propose ZERO a combined approach at Network and Link layers to increase network lifespan while conserving reliability levels by means of probabilistic load balancing techniques.

Lean Sensing: Exploiting Contextual Information for Most Energy-Efficient Sensing

Cyber-physical technologies enable event-driven applications, which monitor in real-time the occurrence of certain inherently stochastic incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is smart parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this paper introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, this paper departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide aggregated contextual information to optimize power consumption, thus going in the opposite way; from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart-parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.

A REVIEW ON ACOUSTIC MONITORING OF DEBRIS FLOW

Debris flows and debris floods are processes that occur in high alpine regions with consequences on infrastructure and settlements. Recently, several studies have been conducted by the authors using a new approach to gather knowledge about debris flows using a combination of two acoustic sensors: seismic sensors and infrasound microphones. Both sensors have been individually used in a number of previous studies. But the potential combination of infrasonic and seismic sensors for monitoring natural hazards, which could take advantage of the benefits of both sensor technologies and the possibility of using this setup for early warning, has not been evaluated to date. As a consequence, in this study the most important characteristics of acoustic signals from debris flows monitored at different locations in the Austrian and Swiss Alps are summarized and possible interfering signals are presented. An approach of using this acoustic signal for early warning will be introduced and evaluated. In addition, the data will be compared with other measurements, such as flow depth, interpretation, verification, and validation of the seismic and infrasonic data. Keywords Debris flow, early warning, infrasound, monitoring, seismic waves Language: en

Optimal Rate Allocation in Cluster-Tree WSNs

In this paper, we propose a solution to the problem of guaranteed time slot allocation in cluster-tree WSNs. Our design uses the so-called Network Utility Maximization (NUM) approach as far as we aim to provide a fair distribution of the available resources. From the point of view of implementation, we extend here the authors’ proposed Coupled-Decompositions Method (CDM) in order to compute the NUM problem inside the cluster tree topology and we prove the optimality of this new extended version of the method. As a result, we obtain a distributed solution that reduces the total amount of signalling information in the network up to a factor of 500 with respect to the classical techniques, that is, primal and dual decomposition. This is possible because the CDM finds the optimal solution with a small number of iterations. Furthermore, when we compare our solution to the standard-proposed First Come First Serve (FCFS) policy, we realize that FCFS becomes pretty unfair as the traffic load in the network increases and thus, a fair allocation of resources can be considered whenever the price to pay in terms of signalling and computational complexity is controlled.

Early Scavenger Dimensioning in Wireless Industrial Monitoring Applications

The industrial Internet era is pushing for even more miniaturized, powerful, and energy efficient devices that seamlessly integrate to the Internet and aim to improve efficiency of industries by monitoring, actuating, or sampling data from machines, infrastructures, and systems. Industrial low power wireless protocols are one of the key enablers of that revolution but still energy consumption is what is limiting ubiquitous deployments of perpetual and unattended devices. The adoption of energy harvesting technologies is enabling autonomously powered control and monitoring systems on industries, infrastructures, and cities. Yet, putting these systems together require a clear understanding of their capabilities and behavior in order to dimension their energy needs and to contribute to the development of a new generation of self-powered ubiquitous devices. Therefore, this paper discusses, through a use case, the tradeoff to reliably dimension scavenger properties to network requirements and application needs, with the main objective to enable industries to optimize the adoption of that technologies while keeping low technical risks.